Jason M Knight

ABOUT

I'm a computational biologist/engineer/bioinformatician with several years experience
with high-dimensional data analysis. I have a penchant for programming and
programming language design with some hardware and entrepreneurial experience
mixed in. I also have a wide range of additional interests and hobbies (cryptocurriencies,
biohacking, etc.).

I am currently finishing my Ph.D. at Texas A&M University in the Genomic Signal Processing laboratory where I apply tools from modern engineering, mathematics, and statistics
in order to understand and treat cancer.

Specifically, I have worked closely with biologists to develop Bayesian statistical
models to uncover multivariate interactions among genes from RNA-Seq data.
This allows us to classify types of cancer better than nonlinear SVM methods
(which themselves are typically considered state of the art). I have also
modeled biological regulatory networks using probabilistic Markov models
that incorporate pathway knowledge from the biology literature as well as
high-throughput data.

In the future, I want to continue using my knowledge and skills to enable principled
approaches towards the detection, classification, and treatment of human
diseases.

RESEARCH/PROJECTS

Every single one of the 40 trillion cells in your body has tens
of thousands of genes which are coordinated in a chaotic symphony of
regulation for your continued living (homeostasis in biological
parlance). For decades now, biologists have been studying this regulatory
network piece by piece through a variety of ingenious techniques and a
mind-boggling amount of effort.

The trouble is, this biological pathway knowledge is incomplete and
sometimes contradictory. Due to this, building predictive models from
this knowledge is difficult. In this work, we used a modeling approach
embracing the uncertainty through a family of Markov chains (which
themselves are a random dynamical system model).

Specifically, we obtained pathways from the biological literature
regarding the NF-kB network which is central to cellular inflammatory
processes. Then constructing a family of Markov chains from these
pathways, we could then make predictions of how the network might evolve
under the deletion of several genes under the effect of various stimuli.
These predictions were then qualitatively validated against additional
biological literature where these experiments were carried out on mouse
gene knockout models.